Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201

MULTI-CHANNEL IMAGE SOURCE SEPARATION BY DICTIONARY UPDATE METHOD

In real world, a large set of mixed signals are available from which each source signal need to be recovered and this problem can be addressed with adaptive dictionary method. In the case of multichannel observations sparsity found to be very useful for source separation. The problem exist is that in most cases the sources are not sparsified in their domain and it will become necessary to sparsify the source by using some known dictionaries. In order to recover the sources successfully a prior knowledge of the sparse domain is required, if not available this problem can be solved by using dictionary learning technique into source separation. The proposed method, a local dictionary is adaptively learned for each source separately along with separation. This approach improves the quality of source separation both in noiseless and different noisy situations. The advantage of this method is that it denoise the sources during separation

Separating Reflection and Transmission Images in the Wild

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201

A Simple Method for Removing Reflection and Distortion from a Single Image

Abstract: This paper deals with a problem of removing reflection and distortion from un-natural images. This will effected in the quality of images. Reflection happens when there is the variation in direction of a wave front at an interface between two different media so that the wave front returns into the medium from which it originated. The law of reflection describes for specular reflection the angle at which wave is reflected equals the angle at which it is incident on the surface. Mirrors exhibit specular reflection. In photograph Distortion will happens when either the properties of the lens or the position of the camera relative to the subject. Here the input contains multiple polarized images with different polarizer angles. The output consists of high quality distortion and reflection separation from images. In this paper proposed a Quality Assessment method Scheme (QAMS) for removing both reflection and distortion from images. Using this QAMS method, the quality of the image can be improved by measuring PSNR and Error Rate

Sparsity and adaptivity for the blind separation of partially correlated sources

Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the sources. However, dealing with real-world data reveals that such assumptions are rarely valid in practice: the signals of interest are more likely partially correlated, which generally hampers the performances of standard BSS methods. In this article, we introduce a novel sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve sparse and partially correlated sources. More precisely, it makes profit of an adaptive re-weighting scheme to favor/penalize samples based on their level of correlation. Extensive numerical experiments have been carried out which show that the proposed method is robust to the partial correlation of sources while standard BSS techniques fail. The AMCA algorithm is evaluated in the field of astrophysics for the separation of physical components from microwave data.Comment: submitted to IEEE Transactions on signal processin

Separation of reflected images using WFLD

Master'sMASTER OF SCIENC

HiMean: A HyGene Approach to Semantic Analysis in a Medical Decision-Making Task

This dissertation makes an exploratory comparison between two semantics models, Latent Semantic Analysis (LSA) and a newly introduced HiMean model based on the HyGene architecture, in a medical decision-making context. Emphasis is placed on using real-world, human decipherable input to produce rational diagnoses. Base rate information is manipulated as a proxy to expertise or learning in different information environments, and outcomes on decision measures are examined. Model performance in terms of correct probe or query identification, alternative hypothesis generation, probe degradation resilience, probability judgments, and diagnostic capability is evaluated. Multidimensional scaling is also employed to investigate two-dimensional projections of the models’ respective semantic spaces. Experimental outcomes reveal that both the LSA and HiMean models, as well as HiMean variants perform well in a variety of conditions. The models produce performance tradeoffs between each other in terms of accuracy, judgment calibration, and robustness to probe error, though not in diagnostic capability. The models are demonstrated to be capable of utilizing non-trained data and producing identification accuracies up to 80%. Generally, both LSA and HiMean prove to be capable decision architectures with a wide variety of potential applications. Some thought is given to future work dedicated to a multi-agent decision system which capitalizes on the strengths of both models